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A provable algorithmic approach to product selection problems for market entry and sustainability

Silei Xu, Yishi Lin, Hong Xie, John C. S. Lui
2014 Proceedings of the 26th International Conference on Scientific and Statistical Database Management - SSDBM '14  
In this paper, we present a general framework for the following product selection problems: (1) k-BSP problem, which is for a manufacturer to enter a competitive market, and (2) k-BBP problem, which is  ...  for a manufacturer to sustain in a competitive market.  ...  [7] first advocated using a microeconomic approach on data mining.  ... 
doi:10.1145/2618243.2618250 dblp:conf/ssdbm/XuLXL14 fatcat:htqawpuv7fh6bkea6djwlagnaq

A Hybrid Feature Selection Approach for Microarray Gene Expression Data [chapter]

Feng Tan, Xuezheng Fu, Hao Wang, Yanqing Zhang, Anu Bourgeois
2006 Lecture Notes in Computer Science  
In this paper, we propose a hybrid approach to combine useful outcomes from different feature selection methods through a genetic algorithm.  ...  The experimental results demonstrate that our approach can achieve better classification accuracy with a smaller gene subset than each individual feature selection algorithm does.  ...  We propose a hybrid approach that combines valuable information from multiple feature selection methods through a genetic algorithm (GA).  ... 
doi:10.1007/11758525_92 fatcat:34w23yiymvhuhhrdw5vbmoswqi

The impact of statistics for benchmarking in evolutionary computation research

Tome Eftimov, Peter Korošec
2018 Proceedings of the Genetic and Evolutionary Computation Conference Companion on - GECCO '18  
Benchmarking theory in evolutionary computation research is a crucial task that should be properly applied in order to evaluate the performance of a newly introduced evolutionary algorithm with performance  ...  In this paper, we evaluate the impact of different already established statistical ranking schemes that can be used for evaluation of performance in benchmarking practice for evolutionary computation.  ...  As a control algorithm, CMA-CSA was selected. In table 6, p-values for each pairwise comparison using both statistical approaches with different statistical criteria are presented.  ... 
doi:10.1145/3205651.3208232 dblp:conf/gecco/EftimovK18 fatcat:bdsuodkdfjbsvmvbpx77ef7oz4

Insights into Exploration and Exploitation Power of Optimization Algorithm Using DSCTool

Peter Korošec, Tome Eftimov
2020 Mathematics  
The pipeline is based on a web-service-based e-Learning tool called DSCTool, which can be used for making statistical analysis not only with regard to the obtained solution values but also with regard  ...  Though this is a good indicator about the performance of the algorithm, it does not provide any information about the reasons why it happens.  ...  Abbreviations The following abbreviations are used in this manuscript: DSC deep statistical comparison eDSC extended deep statistical comparison KS Kolmogorov-Smirnov AD Anderson-Darling FE function  ... 
doi:10.3390/math8091474 fatcat:s4mqxnlmpfbo5kksyuhhc7ogfq

Recommending Collaborative Filtering Algorithms Using Subsampling Landmarkers [chapter]

Tiago Cunha, Carlos Soares, André C. P. L. F. de Carvalho
2017 Lecture Notes in Computer Science  
We propose a set of landmarkers for a Metalearning approach to the selection of Collaborative Filtering algorithms.  ...  As the number of algorithms grows, the selection of the most suitable algorithm for a new task becomes more complex.  ...  Partnership Agreement, and through the Portuguese National Innovation Agency (ANI) as a part of project «FASCOM | POCI-01-0247-FEDER-003506».  ... 
doi:10.1007/978-3-319-67786-6_14 fatcat:6qbb6yjaave4dj7vw2fj2oqaf4

Third Case Study for the Dynamic Multilevel Component Selection

Andreea Vescan
2017 Studia Universitatis Babes-Bolyai: Series Informatica  
The tests performed show the potential of evolutionary algorithms for the dynamic multilevel component selection problem.  ...  This paper deals with the component selection problem with a multilevel system view in a dynamic environment. To validate our approach we have used the case study method.  ...  The Wilcoxon statistical test was used to compare our Genetic Algorithm approach with a Random Search Algorithm: we have statistically significant evidence at α = 0.05 to show that the median is positive  ... 
doi:10.24193/subbi.2017.1.02 fatcat:p24jt57vvzemtaqqeb2tz3hkla

An update on statistical boosting in biomedicine [article]

Andreas Mayr, Benjamin Hofner, Elisabeth Waldmann, Tobias Hepp, Olaf Gefeller, Matthias Schmid
2017 arXiv   pre-print
Statistical boosting algorithms have triggered a lot of research during the last decade.  ...  They combine a powerful machine-learning approach with classical statistical modelling, offering various practical advantages like automated variable selection and implicit regularization of effect estimates  ...  Acknowledgements The authors thank Corinna Buchstaller for her help with the literature search.  ... 
arXiv:1702.08185v1 fatcat:acecmssegzfixh3i66seosnzdm

An Update on Statistical Boosting in Biomedicine

Andreas Mayr, Benjamin Hofner, Elisabeth Waldmann, Tobias Hepp, Sebastian Meyer, Olaf Gefeller
2017 Computational and Mathematical Methods in Medicine  
Statistical boosting algorithms have triggered a lot of research during the last decade.  ...  They combine a powerful machine learning approach with classical statistical modelling, offering various practical advantages like automated variable selection and implicit regularization of effect estimates  ...  Acknowledgments The authors thank Corinna Buchstaller for her help with the literature search.  ... 
doi:10.1155/2017/6083072 pmid:28831290 pmcid:PMC5558647 fatcat:snvv4vj3tzcljj7yejtagw7jja

An efficient statistical feature selection approach for classification of gene expression data

B. Chandra, Manish Gupta
2011 Journal of Biomedical Informatics  
The paper introduces a novel and efficient feature selection approach based on statistically defined effective range of features for every class termed as ERGS (Effective Range based Gene Selection).  ...  A robust feature selection algorithm is required to identify the important genes which help in classifying the samples efficiently.  ...  Acknowledgments We are thankful to anonymous reviewers for their valuable comments. The comments are helpful for improving the manuscript.  ... 
doi:10.1016/j.jbi.2011.01.001 pmid:21241823 fatcat:cbxqjhyozrfcvmbqdftrjwncum

An automated parameter selection approach for simultaneous clustering and feature selection

Vijay Kumar, Jitender K. Chhabra, Dinesh Kumar
2016 Maǧallaẗ al-abḥāṯ al-handasiyyaẗ  
An automated parameter selection approach for simultaneous clustering and feature selection ABSTRACT In this paper, an improved version of Niching Memetic Algorithm for Simultaneous Clustering and Feature  ...  An automated approach is proposed to determine these parameters of NMA_CFS.  ...  Sheng et al. (2008) proposed an approach for simultaneous clustering and feature selection using a niching based memetic algorithm (NMA_CFS).  ... 
doi:10.7603/s40632-016-0014-2 fatcat:ns5rsyu5fbcdrfataubarvgzbe


Oleg I. Sheluhin, Moscow Technical University of Communication and Informatics, Valentina P. Ivannikova, Moscow Technical University of Communication and Informatics
2020 T-Comm  
A comparative analysis of statistical and model-based methods for selecting the quantity and the composition of informative features was performed using the UNSW-NB15 database for machine learning models  ...  It is shown that the most effective among the analyzed methods for feature selection is the statistical method SelectKBest with the function chi2, which allows to obtain a reduced set of features providing  ...  This approach is known as Gini importance or Mean Decrease in Impurity as well. Statistical approach uses statistical methods for feature selection.  ... 
doi:10.36724/2072-8735-2020-14-10-53-60 fatcat:n2i2treqmfce7fxhnhqwwryjuy

Minimum redundancy maximum relevance feature selection approach for temporal gene expression data

Milos Radovic, Mohamed Ghalwash, Nenad Filipovic, Zoran Obradovic
2017 BMC Bioinformatics  
Feature selection, aiming to identify a subset of features among a possibly large set of features that are relevant for predicting a response, is an important preprocessing step in machine learning.  ...  In the proposed approach we compute relevance of a gene by averaging F-statistic values calculated across individual time steps, and we compute redundancy between genes by using a dynamical time warping  ...  Availability of data and materials The datasets used in this study and MATLAB 8.5 source code for TMRMR-C and TMRMR-M algorithms are publically available at: radovicmiloskg/TMRMR.git  ... 
doi:10.1186/s12859-016-1423-9 pmid:28049413 pmcid:PMC5209828 fatcat:r3yvhcnvlvcijjwjzldkhdivtm

Heuristic Search over a Ranking for Feature Selection [chapter]

Roberto Ruiz, José C. Riquelme, Jesús S. Aguilar-Ruiz
2005 Lecture Notes in Computer Science  
In this work, we suggest a new feature selection technique that lets us use the wrapper approach for finding a well suited feature set for distinguishing experiment classes in high dimensional data sets  ...  This heuristic leads to considerably better accuracy results, in comparison to the full set, and other representative feature selection algorithms in twelve well-known data sets, coupled with notable dimensionality  ...  And Table 4 records the running time for each feature selection algorithm, showing two results for each wrapper approach, depending on the learning algorithm chosen.  ... 
doi:10.1007/11494669_91 fatcat:j6rogwtq6bhzppcxttgcszhj2q

Gaussian Based Particle Swarm Optimisation and Statistical Clustering for Feature Selection [chapter]

Mitchell C. Lane, Bing Xue, Ivy Liu, Mengjie Zhang
2014 Lecture Notes in Computer Science  
This paper proposes a new particle swarm optimisation (PSO) algorithm using statistical clustering information to solve feature selection problems.  ...  It maintains the classification performance achieved by the standard PSO for feature selection algorithm, but significantly reduces the number of features and the computational cost.  ...  Due to the page limit, only typical EC based feature selection algorithms and the role of statistics are reviewed here. EC Approaches for Feature Selection. Zhu et al.  ... 
doi:10.1007/978-3-662-44320-0_12 fatcat:xagv7uqf35d6dnnu2tt72lzmwy

The Behavior of Deep Statistical Comparison Approach for Different Criteria of Comparing Distributions

Tome Eftimov, Peter Korošec, Barbara Koroušić Seljak
2017 Proceedings of the 9th International Joint Conference on Computational Intelligence  
Deep Statistical Comparison (DSC) is a recently proposed approach for the statistical comparison of metaheuristic stochastic algorithms for single-objective optimization.  ...  The DSC ranking scheme uses a statistical test for comparing distributions in order to rank the algorithms. DSC was tested using the two-sample Kolmogorov-Smirnov (KS) test.  ...  ACKNOWLEDGEMENTS This work was supported by the project ISO-FOOD, which received funding from the European Union's Seventh Framework Programme for research, technological development and demonstration  ... 
doi:10.5220/0006499900730082 dblp:conf/ijcci/EftimovKK17 fatcat:6wgn24ip3rcvpnmae4xb52ox4m
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